Segmentation evaluation is an important way to improve the performance of algorithms. A gray evaluation model is proposed based on combinational weighting, aiming at the problem that the current index of image segmentation can not reflect the results of segmentation well. Firstly, variation of information, global consistency error, and probabilistic rand index are selected to evaluate the quality of image segmentation. Secondly, a subjective and objective combinational weighting method is proposed which combines Delphi method, forced decision method, and entropy method. The weights not only reflect the subjective preferences of observers, but also highlight the objective differences of images. Finally, the proposed model is used to make a comprehensive evaluation of test images. Experimental results show that the proposed evaluation model is consistent with the subjective evaluation results and the real ground results. Moreover, this model is used to compare the segmentation results of the maximum entropy threshold algorithms based on flower pollination algorithm, genetic algorithm, and shuffled frog leaping algorithm, respectively. The obtained rank is consistent with the result of maximum entropy, which further validates the effectiveness of this model.