Correct, quantile regression is an option. Another is "pure" bootstrapping (you can see by googling something like uncertainty + machine learning + bootstrapping that this is a very active area of current research).

The major problem with bootstrapping is the computational time for big models, since many models need to be fit to obtain a representative distribution of predictions.

Now, if you want more "rigorous" quantification of uncertainty, one option is to go Bayesian using probabilistic programming (PyMC, Stan, TMB), but computational time for large models can be prohibitive. Another option is to "scale down" the complexity to models that might be (on average) a bit less accurate, but provide rigorous uncertainty intervals and good interpretability of results, for example Generalized Additive Models.

A note here is that I saw certain quantification of uncertainty by people who were considered very capable in the ML community that gave me goosebumps, for example since the lower bound of the interval was a negative number and the response variable modeled could not be negative, the uncertainty interval was "cut" at zero (one easy way to deal with it, although it depends on the variable modeled and the model itself, is log-transforming the response—but pay attention to intervals when exp(log(y)) to get back to the natural scale. Another useful interview question.)