Fuse two sources of linear acceleration with a Kalman filter
That depends on how many of the accelerometer parameters (mostly drift and misalignment) you're trying to estimate.

If the IMU and the 'extra' accelerometer were in perfect alignment (and if their statistics are Gaussian), then the optimal combination of their outputs would be a simple weighted sum: $\vec {\hat a} = k_1 \vec a_1 + k_2 \vec a_2$ where (assuming equal scaling), $k_1 = \frac{\sigma_2^2}{\sigma_1^2 + \sigma_2^2}$, $k_2 = 1 - k_1$, and $\sigma_1$ and $\sigma_2$ are the noise variance of each accelerometer.

If you look at that first equation, you'll see that if one accelerometer is markedly quieter than the other, then unless it has other problems (misalignment, scaling, initial offset, etc.) your best bet is to just ignore the noisy accelerometer altogether.  Even at the point where one accelerometer has three times less variance than the other, the ultimate contribution of the "noisy" accelerometer ends up being $1/9^{th}$ of the total -- which is definitely tending into "why bother?" territory.

If you've got some oddball combination, i.e. you've got a really quiet accelerometer with terrible initial offset or misalignment vs. a really noisy one with low offset and misalignment, then life gets more complicated -- in fact, I'd have to ponder on exactly how you'd express that in a way that would make a sensible Kalman filter.