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Having ground truth is critical for evaluating segmentation algorithms and estimating the ground truth from a collection of manual segmentations remains a hard problem. A proper estimation approach should take into account and compensate for the inter-rater variation. In this paper, we conduct an analysis of manual segmentations in order to have a better understanding of the pattern of the variation and investigate whether incorporating such pattern information will improve the ground truth estimation. We propose a level-set based approach that solves the ground truth estimation in a probabilistic formulation. The prior pattern information is incorporated into the estimation model by adding a specially designed term in the energy function. Experiments on both synthetic and real data show that this prior information helps to find a more accurate estimate of the ground truth.