Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, the effectiveness of variance-based uncertainty - computed as pixel-wise variance across stochastic forward passes - in identifying segmentation errors, particularly near tumor boundaries, remains insufficiently studied. This study empirically examines the relationship between variance-based MC Dropout uncertainty and segmentation error in 2D brain tumor MRI segmentation using a U-Net trained under four augmentation settings: none, horizontal flip, rotation, and scaling. Uncertainty was estimated as the pixel-wise variance across 50 stochastic forward passes and correlated with pixel-wise errors using Pearson and Spearman coefficients. Results show weak global correlations (r ~ 0.30-0.38) and negligible boundary correlations (|r| < 0.05). Although differences across augmentations were statistically significant (p < 0.001), they lacked practical relevance. These findings suggest that variance-based MC Dropout uncertainty provides limited cues for global and boundary error localization, and that the choice of uncertainty representation critically affects the utility of MC Dropout in medical image segmentation. Alternative representations such as predictive entropy or mutual information may better capture segmentation errors, particularly at boundaries.