Segmentation of 3D medical images is useful for various medical tasks. However, fully automated segmentation lacks the accuracy required for medical purposes while manual segmentation is too time-consuming. Therefore, an active learning method can be used to generate an accurate segmentation using less user input. The active learning pipeline consists of automatic 3D segmentation, generation of a 3D uncertainty field and an optimization algorithm finding the optimal plane in the uncertainty field. This plane can then be shown to a user to be labeled so that the segmentation can be improved. This process is repeated until the user is satisfied with the output. If the plane is chosen so that it contains more errors, then less user input will be needed. This paper focusses on evaluating different optimization algorithms in the context of this pipeline. The three algorithms that are evaluated are particle swarm optimization, gradient descent and L-BFGS-B. The results of the evaluation show that particle swarm optimization converges the quickest, but to lower values than gradient descent. Gradient descent converges slowly, but to high values. L-BFGS-B converges quickly to values that are as high as those from gradient descent. Therefore, using L-BFGS-B in the pipeline instead of gradient descent will decrease the runtime of the pipeline. Using particle swarm optimization will decrease the runtime even further, but at the cost of requiring more user input to obtain a segmentation of acceptable quality.