To quantify this comparison, the proportion of points that fall within the confidence intervals is calculated, yielding the observed confidence. As the expected confidence level increases, so does the size of the confidence intervals, naturally leading to a higher number of points falling within these expanded intervals. Ideally, the observed confidence will perfectly align with the expected confidence across all intervals, indicating that the model's uncertainty estimates are accurately calibrated. The closer the observed calibration curve aligns with this ideal, the more accurate the uncertainty estimates are considered to be. Calibration curves thus provide a visual and quantitative means to assess if a model's uncertainty estimates are overconfident (falling below the ideal line) or underconfident (above the ideal line).