When prediction errors are primarily linked to approximation uncertainty, deep ensembling can improve model performance . This improvement stems from the averaging effect of combining multiple models. If the individual models' errors are uncorrelated, averaging their predictions helps reduce these errors, leading to more accurate and robust overall performance. However, if ensembling does not yield significant improvements, it suggests that non-variance uncertainties (i.e., aleatoric and/or model uncertainty) are the main sources of error.