Calibration curves serve as a vital tool for evaluating the accuracy of uncertainty estimates in ML models, particularly in the realm of uncertainty prediction. This technique assesses whether the magnitudes of predicted uncertainties align with actual errors, under the assumption that predictions follow a Gaussian distribution. Calibration curves enable the definition of a confidence interval for each prediction, representing the expected confidence level. The essence of this method lies in comparing these intervals with the actual occurrences of data points within them.