Utilizing deep neural networks for automatic segmentation of skin lesion images represents a significant advancement in current research. The issue of class imbalance poses a major challenge in most skin lesion datasets, as skin cancer patients are often much fewer in number compared to patients with other common skin conditions. This disparity results in difficulties for the network to learn features of minority lesion classes, leading to suboptimal segmentation results. To address this problem, we propose the Global Pixel Weighted Focal Loss (GPW-FL) function. Unlike Focal Loss used in the domain of object detection to tackle foreground-background class imbalance, it applies a modulation term to each pixel in an image to adjust its weight. Our approach focuses on reducing the loss assigned to all pixels in well-segmented lesion images during training. Specifically, GPW-FL utilizes Dice loss during training to assess the overall segmentation status of input lesions, enabling the network to pay more attention to poorly segmented skin lesions to improve segmentation performance. To evaluate the effectiveness of GPW-FL, we integrate it into a traditional U-Net segmentation network for training. Experimental results on the ISIC2018 dermoscopic skin lesion dataset and the XJUSL clinical skin lesion dataset demonstrate that our proposed loss function exhibits robustness on class-imbalanced datasets, and segmentation performance surpass those of baseline models and other state-of-the-art segmentation models.