  The risk of collision between resident space objects has significantly
increased in recent years. As a result, spacecraft collision avoidance
procedures have become an essential part of satellite operations. To ensure
safe and effective space activities, satellite owners and operators rely on
constantly updated estimates of encounters. These estimates include the
uncertainty associated with the position of each object at the expected TCA.
These estimates are crucial in planning risk mitigation measures, such as
collision avoidance manoeuvres. As the TCA approaches, the accuracy of these
estimates improves, as both objects' orbit determination and propagation
procedures are made for increasingly shorter time intervals. However, this
improvement comes at the cost of taking place close to the critical decision
moment. This means that safe avoidance manoeuvres might not be possible or
could incur significant costs. Therefore, knowing the evolution of this
variable in advance can be crucial for operators. This work proposes a machine
learning model based on diffusion models to forecast the position uncertainty
of objects involved in a close encounter, particularly for the secondary object
(usually debris), which tends to be more unpredictable. We compare the
performance of our model with other state-of-the-art solutions and a na\"ive
baseline approach, showing that the proposed solution has the potential to
significantly improve the safety and effectiveness of spacecraft operations.
