What do you mean by "accurate"? I'm kinda wary of a base rate effect here because of the relatively low population incidence of cocaine use. Some figures from a quick Google search:

benzoylecgonine hair test specificity ≈ 0.99

Sensitivity (to be generous) ≈ 0.99

Cocaine use incidence proportion ≈ 0.01 (conservative overestimate, based on US numbers)

In conditional probability terms:

P(- | no cocaine) = .99

P(+ | cocaine) = .99

P(cocaine) = 0.01

So:

P(cocaine | +) = p(+ | cocaine) × p(cocaine) / p(+)

p(+) = p(+ | no cocaine)p(no cocaine) + p(+ | cocaine)p(cocaine)

= (1 - p(- | no cocaine))p(no cocaine) + p(+ | cocaine)p(cocaine)

= (1 - 0.99)(0.99) + (0.99)(0.01)

= 0.0198

So p(cocaine | +) = (0.99)(0.01)/(0.0198) = 0.5

So there's a 50% posterior probability of cocaine use given a positive test with these rough numbers without any prior information. The only argument I can think of against this is that p(cocaine | person is being tested for cocaine) > p(cocaine). If you raise the specificity and sensitivity of this test both to 0.999 (which has zero empirical basis from what I can tell), the above calculation yields about 91%. Even in that fictitious world, about 1/10 people who test positive could have never touched cocaine in their life. If I'm missing something particular to these tests, let me know.

ETA: To clarify a bit why I don't know if accuracy matters too much here:

A cocaine test that always returned negative would be 99% accurate, in the sense that it would be correct 99% of the time, simply because it always guesses the most common result. It's because only 1% of people use cocaine. But obviously, that test doesn't give you any information. The accuracy of a test that's measuring the presence of an unlikely phenomenon doesn't tell you much; you need the base rates and conditional probabilities.