A coefficient value of 1 signifies a perfect positive correlation, indicating that a lower error corresponds with lower uncertainty, as expected. Conversely, a coefficient of -1 reveals a perfect negative correlation, suggesting a discrepancy between errors and uncertainties. This methodology focuses on the global relationship between error and uncertainty by relying on the rank of values rather than their precise magnitudes. As a result, Spearman's rank correlation coefficient provides a reliable metric for comparing different ML models since it is unaffected by the scale of standard deviations and does not require assumptions about the distribution of predictions.