Uncertainty estimation is a research field and methodological approach focused on the quantification, characterization, and communication of the inherent variability or lack of certainty associated with data, models, predictions, or system states. It investigates diverse sources of uncertainty, encompassing epistemic uncertainty (due to lack of knowledge or data) and aleatoric uncertainty (due to inherent randomness), employing statistical, probabilistic, and computational techniques to provide measures such as confidence intervals, prediction intervals, or full probability distributions, which are essential for robust decision-making, risk assessment, and scientific validation.