Image-guided surgery systems are often used to provide surgeons with informational support. Due to several unique advantages such as ease of use, real-time image acquisition, and no ionizing radiation, ultrasound is a common intraoperative medical imaging modality used in image-guided surgery systems. To perform advanced forms of guidance with ultrasound, such as virtual image overlays or automated robotic actuation, an ultrasound calibration process must be performed. This process recovers the rigid body transformation between a tracked marker attached to the transducer and the ultrasound image. Point-based phantoms are considered to be accurate, but their calibration framework assumes that the point is in the image plane. In this work, we present the use of an active point phantom and a calibration framework that accounts for the elevational uncertainty of the point. Given the lateral and axial position of the point in the ultrasound image, we approximate a circle in the axial-elevational plane with a radius equal to the axial position. The standard approach transforms all of the imaged points to be a single physical point. In our approach, we minimize the distances between the circular subsets of each image, with them ideally intersecting at a single point. We simulated in noiseless and noisy cases, presenting results on out-of-plane estimation errors, calibration estimation errors, and point reconstruction precision. We also performed an experiment using a robot arm as the tracker, resulting in a point reconstruction precision of 0.64mm.