Simultaneous segmentation of multiple anatomical objects from medical images has become of increasing interest to the medical imaging community, especially when information concerning these objects such as grouping or hierarchical relationships can facilitate segmentation. Single parameter Potts models have often been used to address these multi-region problems, but such parameterization is not sufficient when regions have largely different regularization requirements. These problems can be addressed by introducing smoothing hierarchies with capture grouping relationships at the expense of additional parameterization. Tuning of these parameters to provide optimal segmentation accuracy efficiently is still an open problem in optimal image segmentation. This paper presents two mechanisms, one iterative and one more computationally efficient, for estimating optimal smoothness parameters for any arbitrary hierarchical model based on multi-objective optimization theory. These methods are evaluated using 5 segmentations of the brain from the IBSR database containing 35 distinct regions. The iterative estimator provides equivalent performance to the downhill simplex method, but takes significantly less computation time (93 vs. 431 minutes), allowing for more complicated models to be used without worry as to prohibitive parameter tuning procedures.