Overlap-based metrics such as the Dice similarity coefficient (DSC) penalize segmentation errors more heavily in smaller structures. As organ size differs by sex, this implies that a segmentation error of equal magnitude may result in lower DSCs in women due to their smaller average organ volumes. While previous work has examined sex-based differences in models or datasets, no study has yet investigated the potential bias introduced by the DSC itself. In this work [1], we quantified sexbased differences of the DSC in an idealized setting independent of specific models. The reference was a whole-body MRI dataset of 50 participants (25 male, 25 female) with manual annotations for 40 classes, grouped by volume. Uniformover- and under-segmentationwere simulated by adding or removing fixed voxel margins along structure boundaries. Even minimal errors (i.e., a 1 mm boundary shift) produced systematic DSC differences between sexes. Average DSC differences were around 0.03 for small structures, 0.01 for medium-sized structures; only large structureswere mostly unaffected. These findings show that fairness studies using the DSC should not expect identical scores between the sexes. This does not indicate a flaw in the metric: the DSC correctly assigns greater relative penalty to the same absolute error when the target structure is smaller. However, interpreting such score differences as evidence of model unfairness can be misleading. Even a sex-neutral model can yield different DSC values simply because male and female anatomy differs in volume.