What is Monte Carlo dropout for uncertainty estimation?

Monte Carlo dropout estimates uncertainty by enabling dropout at inference time.

MC dropout treats dropout as approximate Bayesian inference. By running multiple stochastic forward passes with dropout enabled, it produces a distribution of predictions. The variance across predictions is used as uncertainty estimate. It is a practical alternative to full Bayesian neural networks.