In multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) based communication systems, traditional pilot allocation schemes used for channel estimation may not be optimal for target positioning. This limitation motivates us to design a novel allocation scheme that flexibly fulfills requirements in integrated sensing and communication (ISAC) implementations. To address this, we first establish a unified channel state information (CSI) based ISAC model for single-site indoor positioning in uplink MIMO-OFDM systems. To quantify the impact of resource elements (REs) allocated to pilots—across both subcarrier and OFDM symbol dimensions—on positioning performance, we derive the Cramér-Rao lower bounds (CRLBs) of the target parameters for single-site positioning. Subsequently, we jointly optimize the number of pilot REs in the subcarrier dimension and the OFDM symbol dimension to minimize the squared position error bound (SPEB), while satisfying the requirements for communication capacity and velocity estimation. For the formulated mixed integer nonlinear programming (MINLP) problem, we propose an algorithm based on sequential convex approximation (SCA) and penalty functions to convert the non-convex problem into a convex one for efficient solution. Simulation results demonstrate that the proposed algorithm achieves superior SPEB performance compared to benchmark schemes, thereby maximizing time-frequency resource utilization in single-site indoor positioning systems.