We proposed a contour co-tracking method for co-segmentation of image pairs based on active contour model. Our method comprehensively re-models objects and backgrounds signified by level set functions, and leverages Hellinger distance to measure the similarity between image regions encoded by probability distributions. The main contribution are as follows. 1) The new energy functional, combining a rewarding and a penalty term, relaxes the assumptions of co-segmentation methods. 2) Hellinger distance, fulfilling the triangle inequality, ensures a coherence measurement between probability distributions in metric space, and contributes to finding a unique solution to the energy functional. The proposed contour co-tracking method was carefully verified against five representative methods on four popular datasets, i.e., the images pair dataset (105 pairs), MSRC dataset (30 pairs), iCoseg dataset (66 pairs) and Coseg-rep dataset (25 pairs). The comparison experiments suggest that our method achieves the competitive and even better performance compared to the state-of-the-art co-segmentation methods.