Bayesian models solve this problem but they occupy model capacity which practitioners have traditionally preferred to devote to improving point estimates.
I've always found this perspective remarkably misguided. Prediction performance is not everything; it can be extraordinarily powerful to have uncertainty estimates as well.
A lot of machine learning practitioners don't have the statistical sophistication to appreciate this, and as ML becomes increasingly "democratized" I expect the situation will worsen.