First, why think that a coin flip is deterministic? In the nineteenth and, especially, early twentieth century, quite a few scientists and philosophers of science argued that the notion that the world is "governed" by "laws" is a holdover from Christianity. Without that notion of laws, determinism falls away as empty. (What's doing the determining?) And even if we accept the notion of laws, they needn't be deterministic. Ironically, Newtonian mechanics isn't deterministic. Starting with the development of population statistics and statistical mechanics in the 19th century, a number of philosophers and scientists were proponents of "real chance" theories, on which chances or probabilities are real, irreducible features of the world. Peirce and Fischer are notable examples here; I think possibly also Maxwell, although I'm less confident about that. Note that other frequentists — such as the senior Pearson (I always forget whether that's Egon or Karl) — were very strong empiricists, and argued that we should replace the notion of laws with something like "concise descriptions of observations."
There is a sense in which quantum mechanics is deterministic in a way that Newtonian mechanics is not. But even then the observations that I make of random variables or processes (e.g., which path a single photon travels in a double slit experiment) are, in a deep sense, probabilistic. The Stanford Encyclopedia entry on determinism (linked above) is a good place to start reading about determinism.
Second, there's a line of thought in philosophy of science arguing that there are cases in which determinism and indeterminism are empirically equivalent. That is, we would see exactly the same thing if determinism were true as if it were false. There's further controversy over whether, in these cases, indeterminism might be preferable. One good entry into this literature is Charlotte Werndl's "Evidence for the Deterministic or the Indeterministic Description?"
Third, if we abandon the idea that science is about discovering laws, then many philosophers suggest that we think of science as developing maps or models. But maps and models don't need to be perfectly accurate representations of the thing they're representing. They are typically selective — they're only intended to represent certain aspects of their targets — and distorted — introducing simplifications or errors. Maps and models are selective and distorted because doing so makes them more useful as representations. Think of the way the Mercator map projection distorts the polar regions. Doing so allows the map projection to both represent courses of constant bearing as straight lines and use a roughly constant scale over moderately-sized areas. So if you want to know how to get from England to Barbados in the 17th century, you can draw a straight line, read off the course reading, and use a single scale to calculate the distance.
More generally, when we move from laws to models, we arguably need to replace the notion of truth with a notion like "adequacy for purpose." Wendy Parker talks about this move in the context of climate modeling, and Angela Potochnik has a good discussion of the implications of this shift for notions such as objectivity. In this light, your question can be paraphrased as something like "are indeterministic models more adequate for some purposes than deterministic models?" The answer is evidently yes! In statistical mechanics, epidemiology, or quantitative social science, we frequently use indeterministic models to represent systems that include large numbers of individual parts (gas molecules; human beings in a large society) when we don't have a good predictive model of their individual behavior (human beings) or when modeling all of those parts at an individual level would be computationally intractable (gas molecules, especially in Maxwell's day).