Teleoperated robots have enabled humans to manipulate objects in remote environments without requiring physical presence. In this paper we focus on teleoperation of a robotic arm with shared control between the robot and the operator. A model-mediated approach is used to compensate for delays in the communication channel. Position information of the operator's arm is captured and processed to compute the states of a motion prediction model before transmission over a network to be used on the robot's side, allowing for compensation of transmission delays. Model Predictive Control (MPC) and a novel goal prediction algorithm is used to follow the operator's intended motion while reducing the cognitive loads arising from collision avoidance and fine manipulation in the remote environment. We evaluate the proposed method against a baseline pure teleoperation condition with an inverse kinematic controller and observe that the proposed approach improves the overall teleoperation performance in terms of task completion time.