In this paper we address the problem of automatic arrangement of cameras in a 3D system to enhance the performance of depth acquisition procedure. Lacking ground truth or a priori information, a measure of uncertainty is required to assess the quality of reconstruction. The mathematical model of iso-disparity surfaces provides an efficient way to estimate the depth estimation uncertainty which is believed to be related to the baseline length, focal length, panning angle and the pixel resolution in a stereo vision system. Accordingly, we first present analytical relations for fast estimation of the embedded uncertainty in depth acquisition and then these relations, along with the 3D sampling arrangement are employed to define a cost function. The optimal camera arrangement will be determined by minimizing the cost function with respect to the system parameters and the required constraints. Finally, the proposed algorithm is implemented on some 3D models. The simulation results demonstrate significant improvement (up to 35%) in depth uncertainty in the achieved depth maps compared with the traditional rectified camera setup.