While dropout is normally disabled during evaluation, it can be utilized at test time as a heuristic to estimate the uncertainty of a neural network's predictions. By performing multiple forward passes with different dropout masks, the consistency of the model's outputs can be evaluated. If the predictions strongly agree across these diverse iterations, the network is considered to be more confident in its prediction.