Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to change in volume of the specimen during fixation, and misalignment of the histological slices during preparation and digital scanning. In this work we propose a multiple-slice to volume registration method in which a stack of equispaced, uniaxial but unaligned 2D contours, extracted from digitally scanned whole-mount histological slices, is registered to a 3D surface, extracted from a volumetric image of the prostate. Initially, the stack of unaligned contours is coarsely aligned to the surface as a whole. Then, each contour is finely registered to the surface while being confined to its plane along the sectioning axis. We incorporate the method in a particle filtering framework to compensate for the high dimensionality of the search space and multi-modal nature of the problem. Moreover, such framework allows modeling the uncertainty in the segmentation of the contours and surface, in order to derive optimal registration parameters in a Bayesian approach. The proposed algorithm is demonstrated and evaluated on both synthetic and clinical data. The mean area overlap of the registered gland and the segmented histology was found to be 90.2%, with a mean registration error of 1.8mm between visible landmarks.