Uncertainty Estimation involves determining how confident or uncertain a model is about its predictions. This process helps in understanding the reliability and limits of model outputs. For example, in medical diagnosis, it's crucial to measure uncertainty to ensure that potential risks are communicated accurately. It often uses techniques like Bayesian Neural Networks or Monte Carlo Dropout to capture uncertainty in prediction tasks.