Considering the recent progress in the development of practical applications in the field of image processing, it is increasingly important to develop new, efficient and more reliable algorithms to solve an image segmentation problem. To this end, various fusion-based segmentation approaches which use consensus clustering, and which are based on the optimization of a single criterion, have been proposed. One of the greatest challenges with these approaches is to select the best fusion criterion, which gives the best performance for the image segmentation model. In this paper, we propose a new fusion model of image segmentation based on multi-objective optimization, which aims to overcome the limitation and bias caused by a single criterion, and to provide a final improved segmentation. To address the ill-posedness for the search of the best criterion, the proposed fusion model combines two conflicting and complementary criteria for segmentation fusion, namely, the region-based variation of information (VoI) criterion and the contour-based F-measure (precision-recall) criterion using an entropy-based confidence weighting factor. To optimize our energy-based model, we propose an extended local optimization procedure based on superpixels and derived from the iterative conditional mode (ICM) algorithm. This new multi-objective median partition-based approach, which relies on the fusion of inaccurate, quick and spatial clustering results, has emerged as an appealing alternative to the use of traditional segmentation fusion models which exist in the literature. We perform experiments using the Berkeley database with manual ground truth segmentations, and the results clearly show the feasibility and efficiency of the proposed methodology.