Intensity inhomogeneity, which is also called bias field, is ubiquitous in digital images. The causes of intensity inhomogeneity are complex and include uneven illumination and defects of imaging equipment. For images with local intensity inhomogeneity, an array of existing segmentation algorithms has poor performance on efficiency, accuracy, or initial robustness. To tackle this problem, an active contour model based on local prefitting bias estimation is proposed. The bias field is approximated through a new function based on a mean filtering algorithm, which can credibly represent the distribution of bias field of an input image. Then, the bias field is incorporated into the optimized energy functional based on the level set method to implement the segmentation process. Specifically, the bias field is computed before iterations and the mean filtering algorithm is much faster than traditional clustering algorithm, so the efficiency is greatly raised. Moreover, a new regularization function is formulated to improve the robustness of the initial contour and noise. Comparing with some traditional models, the proposed model achieves better results on some challenging images.