This paper presents a novel regularization method that enhances segmentation in neural networks by refining the learning process rather than modifying the architecture. The approach incorporates mutual information as an auxiliary learning objective, where an additional network maximizes shared information between input and output representations. This optimization improves weight adjustments during training, enabling the model to capture structural and contextual relationships more effectively. Experiments on multiple benchmarks demonstrate that this method significantly improves segmentation accuracy, enhancing both large objects and smaller, densely crowded regions. By mitigating the tendency of neural networks to produce overly linear segmentations, it encourages more adaptive, nonlinear contours. Consequently, segmentation masks achieve superior object delineation, improving precision and coherence in separated segments. These findings highlight the efficacy of mutual information in guiding efficient weight updates, enhancing the precision of convolutional segmentation models such as U-Net, and improving the learning of nonlinear features without requiring architectural modifications.