In this paper, we propose an anterior cruciate ligament (ACL) segmentation method in knee MR images using graph cuts with intensity and shape priors. Our method consists of two steps. First, object and background seeds for graph cuts are extracted using adaptive thresholding based on Gaussian mixture model and morphological operation on coronal and sagittal planes. Second, graph cuts are performed to segment ACL with intensity and shape priors information of extracted object and background seeds. In knee MR images, since ACL shares similar intensity with near soft tissues and some of these tissues e.g. posterior cruciate ligament (PCL) are even adjacent to ACL, leakage to these tissues occurs when an intensity-based segmentation is performed. To solve this problem, we propose the technique of representing shape priors from extracted object and background seeds, not from segmented images, and reflecting these shape priors to the graph cuts. To evaluate the performance of our method, visual inspection and accuracy evaluation were performed. Compared to the results of original graph cuts, experimental results of our method show improved segmentation accuracy without leakage into neighboring soft tissues by applying shape priors to the graph cuts.