subset $\mathbf{S}$ of the ground truth segmentation .

\(2) A menu-driven user input scheme as in [@rupprecht2015image; @udupa1997multiple] limits the user’s scope of action. Users trade distinct control over the segmentation outcome for more guidance provided by the system. The locations or the shapes of newly created scribbles are fixed before presentation to the user. It is challenging to achieve an exact segmentation result using a method from this category. Rupprecht et al. [@rupprecht2015image] describe significant deficits in finding small objects and outline a tendency of the system to automatically choose seed point locations near the object border, which cannot be labeled by most users’ visual inspection and would therefore not have been selected by the users themselves. Advantages of user input are the high level of abstraction of the process, enabling efficient guidance for inexperienced users in their decision which action to perform for an optimal segmentation outcome (regarding accuracy over time or number of interactions) [@olabarriaga1999human; @olabarriaga2001interaction].

Generation of Representative User Input
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Nickisch et al. [@nickisch2010learning] describe crowd sourcing and user studies as two methods to generate plausible user input data. The cost efficient crowd sourcing method often lacks control and knowledge of the users’ motivation. Missing 