Accurate skin lesion segmentation is of great significance for improving the quantitative analysis of skin cancer from dermoscopic images. However, lesion segmentation remains a challenging problem due to the large differences in color, location, size, shape, and boundary contrast of lesions. In order to solve these difficulties, we construct a novel lesion segmentation algorithm based on martingale, which combines local and global information of the images. In order to combine the global information of the images in the segmentation process, we build a newly defined random power martingale (RPM) based on the statistical and structural features of the images. The unbiased nature of the martingale process optimizes the subtle boundaries and structural changes in the dermoscopic images. We compare our method with different outstanding algorithms and analyze them using some commonly used evaluation indexes in the International Skin Imaging Collaboration 2016 (ISIC-16) skin lesion dataset. Visualization results and quantitative evaluation show that our method can achieve superior performance.