In this paper, we propose a fast numerical scheme to estimate Partition Functions (PF) of symmetric Potts fields. Our strategy is first validated on 2D two-color Potts fields and then on 3D two- and three-color Potts fields. It is then applied to the joint detection-estimation of brain activity from functional Magnetic Resonance Imaging (fMRI) data, where the goal is to automatically recover activated, deactivated and inactivated brain regions and to estimate region-dependent hemodynamic filters. For any brain region, a specific 3D Potts field indeed embodies the spatial correlation over the hidden states of the voxels by modeling whether they are activated, deactivated or inactive. To make spatial regularization adaptive, the PFs of the Potts fields over all brain regions are computed prior to the brain activity estimation. Our approach is first based upon a classical path-sampling method to approximate a small subset of reference PFs corresponding to prespecified regions. Then, we propose an extrapolation method that allows us to approximate the PFs associated to the Potts fields defined over the remaining brain regions. In comparison with preexisting methods either based on a path-sampling strategy or mean-field approximations, our contribution strongly alleviates the computational cost and makes spatially adaptive regularization of whole brain fMRI datasets feasible. It is also robust against grid inhomogeneities and efficient irrespective of the topological configurations of the brain regions.