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Contours are used in radiotherapy treatment planning to identify regions to be irradiated with high dose and regions to be spared. Therefore, any contouring uncertainty influences the whole treatment. Even though this is the biggest remaining source of uncertainty when daily IGRT or adaptation is used, it has not been accounted for quantitatively in treatment planning. Using probabilistic planning allows to directly account for contouring uncertainties in plan optimisation. The first step is to create an algorithm that can generate many realistic contours with variation matching actual inter-observer variation. We propose a methodology to generate random contours, based on measured spatial inter-observer variation, IOV, and a single parameter that controls its geometrical dependency: alpha, the width of the 3D Gaussian used as point spread function (PSF). We used a level set formulation of the median shape, with the level set function defined as the signed distance transform. To create a new contour, we added the median level set and a noise map which was weighted with the IOV map and then convolved with the PSF. Thresholding the level set function reconstructs the newly generated contour. We used data from 18 patients from the golden atlas, consisting of five prostate delineations on T2-w MRI scans. To evaluate the similarity between the contours, we calculated the maximum distance to agreement to the median shape (maxDTA), and the minimum dose of the contours using an ideal dose distribution. We used the two-sample Kolmogorov-Smirnov test to compare the distributions for maxDTA and minDose between the generated and manually delineated contours. Only alpha=0.75cm produced maxDTA and minDose distributions that were not significantly different from the manually delineated structures. Accounting for the PSF is essential to correctly simulate inter-observer variation.