Medical image segmentation plays a vital role in numerous applications and has gained significant attention since the introduction of the U-Net model, which enabled convolutional neural networks to achieve high performance with manageable computational costs. Recently, attention mechanisms have emerged as a promising approach to enhance model performance by emphasizing relevant features while suppressing irrelevant ones. This study explores the integration of channel and spatial attention mechanisms into the U-Net architecture, evaluating their impact on segmentation performance and computational cost. Experiments conducted on six public medical imaging datasets demonstrated performance improvements, with Intersection over Union (IoU) gains ranging from 1.62% to 33.66% compared to the original U-Net. These results highlight the potential of attention mechanisms to significantly improve the efficiency and effectiveness of medical image segmentation models.