The sensors cannot both have the high accuracy because one or both of them are clearly wrong in your scenario, meaning the estimated accuracy of one or both sensors is clearly wrong.
If your estimate of the accuracy of the sensor is wrong, you are giving a very high accuracy weight to the garbage going in and you will get garbage out.
One of the really interesting things about a Kalman filter is that it improves its estimate of the accuracy of the input sensors over time. In your scenario, over time the Kalman filter would "learn" which sensor is lying and adjust its accuracy estimate down.
> over time the Kalman filter would "learn" which sensor is lying and adjust its accuracy estimate down
No, it won't. The plain old Kalman filter believes precisely what you tell it. In this scenario, the Kalman filter will oscillate about the mean of the two measurements, with the amplitude of that oscillation depending on the relative sizes of the measurement variance and estimate variance. A smaller process noise will cause the estimate covariance to shrink faster, which will dampen the oscillation faster. The oscillation will eventually settle on some minimum amplitude.
An important point here that many don't realize is that the covariance of the Kalman filter is completely independent of the residual. Go ahead, look at the equations - covariance is a function of the measurement model, the prior covariance, and the measurement covariance. The actual measurement doesn't matter.
If your estimate of the accuracy of the sensor is wrong, you are giving a very high accuracy weight to the garbage going in and you will get garbage out.
One of the really interesting things about a Kalman filter is that it improves its estimate of the accuracy of the input sensors over time. In your scenario, over time the Kalman filter would "learn" which sensor is lying and adjust its accuracy estimate down.